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Results: We present VARiD a probabilistic method for variation detection from both letter- and color-space reads simultaneously.
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The trends shown by these different methods for variation in porosity with respect to feedstock characteristics were in agreement.
The method accounted for variation due to differences in recombination, as well as phenotypic variation for each model.
For the Goldberg method, values for variation in rEI, BMR, and PAL as suggested by Black (2000b) were applied to classify misreporting.
The area under the curve method allows for variation in follow-up schedules for the different study groups and also variation in the times at which women completed questionnaires.
The modelling approach described here demonstrates a useful method for characterising variation in ρg so that more accurate estimates of MOE can be made using acoustic methods.
The mixed effects modelling approach described here demonstrates a useful method for characterising variation in ρg allowing more accurate estimates of MOE to be made using acoustic methods.
Mixed-effects models entail a rigorous statistical method for quantifying variation between repeated measurements from the same individual.
As illustrated by the number of high quality SNPs detected, transcriptome or genomic sequencing is also a rapid method for detecting variation in a population.
Although this type of analysis does not appear to be widely used in genomic analyses (but see Naya et al. 2006), it is a powerful method for analyzing variation within and between species.
Since Tier 3 methods account for variation observed within biomes and countries, the representativeness of the carbon estimates is higher than those associated with Tier 1 and 2 methodologies [32, 33].
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